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Record W2262013534 · doi:10.1088/1758-5090/8/1/015008

Investigation of the hydrodynamic response of cells in drop on demand piezoelectric inkjet nozzles

2016· article· en· W2262013534 on OpenAlexafffund
Eric Cheng, Haoran Yu, Ali Ahmadi, Karen C. Cheung

Bibliographic record

VenueBiofabrication · 2016
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNozzleSuspension (topology)Drop (telecommunication)PiezoelectricityRheologyReflection (computer programming)Inkjet printingBiofabricationNanotechnologyBiomedical engineeringComposite materialComputer scienceMechanical engineeringInkwellTissue engineeringEngineering

Abstract

fetched live from OpenAlex

Cell motion within a liquid suspension inside a piezoelectrically actuated, cylindrical inkjet printhead was studied using high speed imaging and a low depth of field setup. For each ejected droplet, a cell within the inkjet nozzle was observed to exhibit one of three possible behaviors which are termed: cell travel, cell ejection and cell reflection. Cell reflection is an undesirable phenomenon which may adversely affect an inkjet's capability in dispensing cells and a possible reason why it was previously reported that the rate of cells dispensed did not follow the expected Poisson distribution. Through the study of the cells motions, it was hypothesized that the rheological properties of the media in the cell suspension play an important role in influencing the cell behaviors exhibited. This was experimentally studied with the tracking of cells within the inkjet nozzle in a 10% w/v Ficoll PM400 cell suspension. The effect of cell reflection was eliminated using the higher density and viscosity Ficoll PM400 suspension. The presented work is the first in-depth study of the cell behaviors occurring within a piezoelectric inkjet nozzle during the printing process. The understanding of the hydrodynamics during a droplet ejection and its effect on the suspended cells are imperative towards achieving reliable cell dispensing for biofabrication applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.231
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations66
Published2016
Admission routes2
Has abstractyes

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